One of the desires of goose (well for some of us) was to avoid the constant asking for permissions, delegating all the decisions to end users in an attempt to keep agent execution of tools safe. Sometimes that gets pretty noisy and annoying and ends up being less secure when you get tired of reading and approving.

You can of course adjust settings as you see fit, but it is nice to consider how things could be made safe without assuming that you can interrupt the user constantly for permission, especially around things they may not currently have the context for (in their head!)

In goose there are layers of things you can enable, but we wanted to also think about general solutions when we observed agents (of all kinds) being really helpful, and as a side effect, being accidentally harmful. This birthed "adversary mode" where the idea is: why not use another agent to fight fire with fire. Agents want to be helpful, and they can be oriented to help the user, but another one can be oriented to protect against the agent "helping" the user, to keep things in policy, and safe.

You can use your subscriptions for codex, claude and gemini now with goose, thanks to ACP! (Agent Client Protocol). Codex is also special in that you can login directly to chatgpt - nothing else needs to be installed.

Gemini natively supports ACP, so it now works with a gemini acp provider in goose. At the time of writing, claude requires just one utility installed just once.

Raise your hand if you thought WebMCP was just an MCP server. Guilty as charged. I did too. It turns out it's a W3C standard that uses similar concepts to MCP. Here's what it actually is.

If you're anything like me, deciding what to eat for lunch is harder than it should be. Now add dietary restrictions on top of that (I'm coeliac so have to eat gluten-free) and suddenly finding a restaurant becomes a whole research project. Searching menus, cross-referencing reviews, checking if that one sandwich actually has gluten in it... it's exhausting.

What if your AI agent could just handle all of that for you?

We launched the goose grant program awarding $100K grants for developers building the future of agentic AI. We're looking for ambitious, open source projects that push goose into new territory, and today, We're thrilled to introduce one of our grant recipients: Goose In A Pond, a project that's taking goose off the desktop and into your home.

goose v1.25.0 is here, and it's one of our most significant releases yet. This version brings macOS sandboxing for enhanced security, a major architectural simplification with the unified summon extension, rich UI rendering for MCP apps, and a wave of improvements to agentic CLI providers. Whether you're running goose Desktop or the CLI, there's something in this release for you.

Let's break down what's new.

On New Year's Day 2026, while many were recovering from the night before, a different kind of hangover took hold of every AI-pilled, chronically online software engineer. Steve Yegge published a new blog post: "Welcome to Gas Town." Some walked away inspired to finally use their agents optimally; others were just plain confused. If you're like me, you felt a bit of both.

Yegge's 34 minute post is a sprawling vision filled with futuristic ideas, playful characters, and enough side tangents to make your head spin. But underneath the lore is a massive architectural shift. I want to take a step back and simplify the "Big Idea" for everyone: Gas Town is a philosophy and a proof of concept to help people coordinate multiple agents working together.

I attended one shot prompting’s funeral.

There were no tears. Just a room full of developers quietly pretending they weren’t taking shots the night before. Because if we’re being honest, everyone saw this coming and couldn’t be happier it was over.

Saying “one shot prompting is dead” isn’t revolutionary. It’s just catching up to what builders have been experiencing for months.

Agents fundamentally changed how we program. They enable developers to move faster by disintermediating the traditional development workflow. This means less time switching between specialized tools and fewer dependencies on other teams. Now that agents can execute complicated tasks, developers face a new challenge: using them effectively over long sessions.

The biggest challenge is context rot. Because agents have limited memory, a session that runs too long can cause them to "forget" earlier instructions. This leads to unreliable outputs, frustration, and subtle but grave mistakes in your codebase. One promising solution is Code Mode.

What is rp-why?​

rp-why is your personal AI collaboration coach. It answers two critical questions:

1. Are you using the most effective AI tools for your work?
2. Are you asking questions that demonstrate cognitive depth?

Think of it as a fitness tracker for your AI practice—it shows you where you are, where you could be, and how to get there.

Want the theory? Check out Measuring the Cognitive Complexity of Human-AI Collaboration.

Want to use it? Keep reading.